from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-04 14:06:45.937892
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Fri, 04, Dec, 2020
Time: 14:06:49
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.2188
Nobs: 130.000 HQIC: -44.3973
Log likelihood: 1368.10 FPE: 2.34066e-20
AIC: -45.2040 Det(Omega_mle): 1.20137e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.552237 0.183893 3.003 0.003
L1.Burgenland 0.131985 0.086664 1.523 0.128
L1.Kärnten -0.308441 0.073513 -4.196 0.000
L1.Niederösterreich 0.077327 0.207678 0.372 0.710
L1.Oberösterreich 0.290974 0.172697 1.685 0.092
L1.Salzburg 0.151593 0.087745 1.728 0.084
L1.Steiermark 0.076556 0.124618 0.614 0.539
L1.Tirol 0.171752 0.082676 2.077 0.038
L1.Vorarlberg 0.021342 0.080098 0.266 0.790
L1.Wien -0.138990 0.164684 -0.844 0.399
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.579183 0.236183 2.452 0.014
L1.Burgenland -0.002440 0.111307 -0.022 0.983
L1.Kärnten 0.335230 0.094416 3.551 0.000
L1.Niederösterreich 0.108146 0.266732 0.405 0.685
L1.Oberösterreich -0.212742 0.221803 -0.959 0.337
L1.Salzburg 0.188705 0.112695 1.674 0.094
L1.Steiermark 0.234137 0.160053 1.463 0.144
L1.Tirol 0.140259 0.106185 1.321 0.187
L1.Vorarlberg 0.211002 0.102874 2.051 0.040
L1.Wien -0.559243 0.211512 -2.644 0.008
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.335513 0.079864 4.201 0.000
L1.Burgenland 0.101209 0.037638 2.689 0.007
L1.Kärnten -0.026244 0.031926 -0.822 0.411
L1.Niederösterreich 0.111418 0.090194 1.235 0.217
L1.Oberösterreich 0.280148 0.075001 3.735 0.000
L1.Salzburg -0.014441 0.038107 -0.379 0.705
L1.Steiermark -0.054181 0.054121 -1.001 0.317
L1.Tirol 0.098865 0.035906 2.753 0.006
L1.Vorarlberg 0.141042 0.034786 4.055 0.000
L1.Wien 0.036115 0.071521 0.505 0.614
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.189265 0.094259 2.008 0.045
L1.Burgenland 0.000403 0.044422 0.009 0.993
L1.Kärnten 0.030736 0.037681 0.816 0.415
L1.Niederösterreich 0.049285 0.106451 0.463 0.643
L1.Oberösterreich 0.373620 0.088520 4.221 0.000
L1.Salzburg 0.087024 0.044976 1.935 0.053
L1.Steiermark 0.202356 0.063876 3.168 0.002
L1.Tirol 0.036243 0.042378 0.855 0.392
L1.Vorarlberg 0.112980 0.041056 2.752 0.006
L1.Wien -0.083711 0.084413 -0.992 0.321
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.705239 0.200878 3.511 0.000
L1.Burgenland 0.062771 0.094668 0.663 0.507
L1.Kärnten -0.015146 0.080303 -0.189 0.850
L1.Niederösterreich -0.103277 0.226860 -0.455 0.649
L1.Oberösterreich 0.093441 0.188648 0.495 0.620
L1.Salzburg 0.034149 0.095849 0.356 0.722
L1.Steiermark 0.123958 0.136128 0.911 0.363
L1.Tirol 0.230619 0.090312 2.554 0.011
L1.Vorarlberg 0.040123 0.087497 0.459 0.647
L1.Wien -0.148227 0.179895 -0.824 0.410
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.232455 0.137897 1.686 0.092
L1.Burgenland -0.052099 0.064987 -0.802 0.423
L1.Kärnten -0.017852 0.055125 -0.324 0.746
L1.Niederösterreich 0.164514 0.155733 1.056 0.291
L1.Oberösterreich 0.397742 0.129502 3.071 0.002
L1.Salzburg -0.039039 0.065798 -0.593 0.553
L1.Steiermark -0.055242 0.093448 -0.591 0.554
L1.Tirol 0.202493 0.061997 3.266 0.001
L1.Vorarlberg 0.049064 0.060064 0.817 0.414
L1.Wien 0.131031 0.123493 1.061 0.289
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.243928 0.175729 1.388 0.165
L1.Burgenland 0.059640 0.082816 0.720 0.471
L1.Kärnten -0.081708 0.070249 -1.163 0.245
L1.Niederösterreich -0.096599 0.198459 -0.487 0.626
L1.Oberösterreich -0.080619 0.165030 -0.489 0.625
L1.Salzburg 0.011180 0.083850 0.133 0.894
L1.Steiermark 0.365422 0.119086 3.069 0.002
L1.Tirol 0.541709 0.079006 6.857 0.000
L1.Vorarlberg 0.234615 0.076542 3.065 0.002
L1.Wien -0.191644 0.157373 -1.218 0.223
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.048880 0.202683 0.241 0.809
L1.Burgenland 0.033716 0.095519 0.353 0.724
L1.Kärnten -0.067623 0.081024 -0.835 0.404
L1.Niederösterreich 0.200371 0.228899 0.875 0.381
L1.Oberösterreich 0.047170 0.190343 0.248 0.804
L1.Salzburg 0.232070 0.096711 2.400 0.016
L1.Steiermark 0.177448 0.137352 1.292 0.196
L1.Tirol 0.056767 0.091124 0.623 0.533
L1.Vorarlberg 0.019452 0.088283 0.220 0.826
L1.Wien 0.259154 0.181511 1.428 0.153
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.617839 0.112403 5.497 0.000
L1.Burgenland -0.018643 0.052972 -0.352 0.725
L1.Kärnten -0.003953 0.044934 -0.088 0.930
L1.Niederösterreich -0.058415 0.126941 -0.460 0.645
L1.Oberösterreich 0.288718 0.105559 2.735 0.006
L1.Salzburg 0.005831 0.053633 0.109 0.913
L1.Steiermark 0.009224 0.076172 0.121 0.904
L1.Tirol 0.077780 0.050535 1.539 0.124
L1.Vorarlberg 0.189420 0.048959 3.869 0.000
L1.Wien -0.098925 0.100661 -0.983 0.326
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.096816 -0.056538 0.180484 0.234013 0.007349 0.069327 -0.121830 0.117058
Kärnten 0.096816 1.000000 -0.058803 0.182435 0.092317 -0.168530 0.196219 0.024158 0.268495
Niederösterreich -0.056538 -0.058803 1.000000 0.240443 0.051986 0.165220 0.076815 0.055451 0.352072
Oberösterreich 0.180484 0.182435 0.240443 1.000000 0.254227 0.268921 0.072211 0.067137 0.048177
Salzburg 0.234013 0.092317 0.051986 0.254227 1.000000 0.129787 0.047441 0.097984 -0.060364
Steiermark 0.007349 -0.168530 0.165220 0.268921 0.129787 1.000000 0.086138 0.083737 -0.191672
Tirol 0.069327 0.196219 0.076815 0.072211 0.047441 0.086138 1.000000 0.133472 0.098305
Vorarlberg -0.121830 0.024158 0.055451 0.067137 0.097984 0.083737 0.133472 1.000000 0.077518
Wien 0.117058 0.268495 0.352072 0.048177 -0.060364 -0.191672 0.098305 0.077518 1.000000